Popular GitHub Repositories for Concepts

A roadmap connecting many of the most important concepts in machine learning, how to learn them and what tools to use to perform them.

A mindmap summarising Machine Learning concepts, from Data Analysis to Deep Learning.

This repository offers a comprehensive collection of tutorials and implementations for Prompt Engineering techniques, ranging from fundamental concepts to advanced strategies. It serves as an essential resource for mastering the art of effectively communicating with and leveraging large language models in AI applications.

A comprehensive machine learning repository containing 30+ notebooks on different concepts, algorithms and techniques.

ALICE (Automated Learning and Intelligence for Causation and Economics) is a Microsoft Research project aimed at applying Artificial Intelligence concepts to economic decision making. One of its goals is to build a toolkit that combines state-of-the-art machine learning techniques with econometrics in order to bring automation to complex causal inference problems. To date, the ALICE Python SDK (econml) implements orthogonal machine learning algorithms such as the double machine learning work of Chernozhukov et al. This toolkit is designed to measure the causal effect of some treatment variable(s) t on an outcome variable y, controlling for a set of features x.

ManimML is a project focused on providing animations and visualizations of common machine learning concepts with the Manim Community Library.

Master the fundamentals of machine learning, deep learning, and mathematical optimization by building key concepts and models from scratch using Python.

My implementation of the original transformer model (Vaswani et al.). I've additionally included the playground.py file for visualizing otherwise seemingly hard concepts. Currently included IWSLT pretrained models.

Notes & exercise solutions of Part I from the book: "Hands-On ML with Scikit-Learn, Keras & TensorFlow: Concepts, Tools, and Techniques to Build Intelligent Systems" by Aurelien Geron

Achieve the llama3 inference step-by-step, grasp the core concepts, master the process derivation, implement the code.

Official implementation for "Break-A-Scene: Extracting Multiple Concepts from a Single Image" [SIGGRAPH Asia 2023]

Machine Learning notebooks for refreshing concepts.

Implements common data science methods and machine learning algorithms from scratch in python. Intuition and theory behind the algorithms is also discussed.

The first machine learning framework that encourages learning ML concepts instead of memorizing class functions.

Low-rank adaptation for Erasing COncepts from diffusion models.

Curated list of my reads, implementations and core concepts of Artificial Intelligence, Deep Learning, Machine Learning by best folk in the world.

A set of lessons aimed at anyone learning LLM and generative AI concepts, with sections on operations and security, as well as development.

A Survey on Explainable Reinforcement Learning: Concepts, Algorithms, Challenges

A curated list of mathematics documents ,Concepts, Study Materials , Algorithms and Codes available across the internet for machine learning and deep learning

An attempt at the implementation of Glom, Geoffrey Hinton's new idea that integrates concepts from neural fields, top-down-bottom-up processing, and attention (consensus between columns), for emergent part-whole heirarchies from data